K- Cluster Diversity Analysis among Potato (Solanum tuberosum L.) Accessions for Growth, Yield and Processing Quality Traits

Abstract

In India, there is a great scope for cultivation of potato suitable for processing and it has opened a new dimension for development of agro-based industries in the country. For processing, certain morphological, bio-chemical and physico-chemical attributes are necessary in potato varieties to meet the industry requirements. The varieties recommended for one location and management conditions may not be the same for other location for production and processing. However, the yield and processing characteristics of available potato genotypes are unidentified locally despite the increasing demand of processing varieties. In the present study, K-means clustering is mainly used to assess the diversity and to identify accessions with suitable processing quality traits. The field experiment was conducted at HREC, Hassan during 2021-22. Clustering analysis of thirty-five genotypes based on twenty-three different growth, yield and processing quality parameters classified entries into eight different clusters. The maximum inter cluster distance was observed between the clusters IV and VII (258.00) followed by cluster V and VI (254.92). The minimum inter cluster distance was recorded between the clusters II and VII (41.73). Among the genotypes, genotypes grouped in cluster I (Sagitta, FC-1, FC-5 and Kufri Surya) and II (Tauras, MP/09-45, Atlantic, Kufri Chipsona-1, Kufri Girdhari, FC-3 and Kufri Chipsona-4) were found suitable for processing. Genotypes in cluster I had highest tuber shape scoring (4.63) whereas, cluster V (2.88) was lowest. Dry matter was highest for genotypes in cluster II (22.50) and lowest in cluster-V (18.14). These results suggest that the genotypes grouped in these clusters may be used as potential parental lines for hybridization programmes to develop desirable genotype suitable for cultivation under rainfed conditions for higher yield with processing quality grade tubers.